Rapid protein-ligand docking using soft modes from molecular dynamics simulations to account for protein deformability: binding of FK506 to FKBP

Most current docking methods to identify possible ligands and putative binding sites on a receptor molecule assume a rigid receptor structure to allow virtual screening of large ligand databases. However, binding of a ligand can lead to changes in the receptor protein conformation that are sterically necessary to accommodate a bound ligand. An approach is presented that allows relaxation of the protein conformation in precalculated soft flexible degrees of freedom during ligand-receptor docking. For the immunosuppressant FK506-binding protein FKBP, the soft flexible modes are extracted as principal components of motion from a molecular dynamics simulation. A simple penalty function for deformations in the soft flexible mode is used to limit receptor protein deformations during docking that avoids a costly recalculation of the receptor energy by summing over all receptor atom pairs at each step. Rigid docking of the FK506 ligand binding to an unbound FKBP conformation failed to identify a geometry close to experiment as favorable binding site. In contrast, inclusion of the flexible soft modes during systematic docking runs selected a binding geometry close to experiment as lowest energy conformation. This has been achieved at a modest increase of computational cost compared to rigid docking. The approach could provide a computationally efficient way to approximately account for receptor flexibility during docking of large numbers of putative ligands and putative docking geometries.     

Flexible ligand docking to multiple receptor conformations: a practical alternative

State of the art docking algorithms predict an incorrect binding pose for about 50-70% of all ligands when only a single fixed receptor conformation is considered. In many more cases, lack of receptor flexibility results in meaningless ligand binding scores, even when the correct pose is obtained. Incorporating conformational rearrangements of the receptor binding pocket into predictions of both ligand binding pose and binding score is crucial for improving structure-based drug design and virtual ligand screening methodologies. However, direct modeling of protein binding site flexibility remains challenging because of the large conformational space that must be sampled, and difficulties remain in constructing a suitably accurate energy function. Here we show that using multiple fixed receptor conformations, either experimentally determined by crystallography or NMR, or computationally generated, is a practical shortcut that may improve docking calculations. In several cases, such an approach has led to experimentally validated predictions.     

Identification of optimum computational protocols for modeling the aryl hydrocarbon receptor (AHR) and its interaction with ligands

The aryl hydrocarbon receptor (AHR) is one of the principal xenobiotic receptors in living organisms and is responsible for interacting with several drugs and environmental toxins, most notably tetrachlorodibenzodioxin (TCDD). Binding of diverse agonists to AHR initiates an extensive set of downstream gene expression responses and thus identifies AHR among a key set of proteins responsible for mediating interactions between living organisms and foreign molecules. While extensive biochemical investigations on the interaction of AHR with ligands have been carried out, studies comparing the abilities of specific computational algorithms in explaining the potency of known AHR ligands are lacking. In this study we use molecular dynamics simulations to identify a physically realistic conformation of the AHR that is relevant to ligand binding. We then use two sets of existing data on known AHR ligands to evaluate the performance of several docking and scoring protocols in rationalizing the potencies of these ligands. The results identify an optimum set of protocols that could prove useful in future AHR ligand discovery and design as a target or anti-target. Exploration of the details of these protocols sheds light on factors operating in modeling AHR ligand binding.     

Testing a flexible-receptor docking algorithm in a model binding site

Sampling receptor flexibility is challenging for database docking. We consider a method that treats multiple flexible regions of the binding site independently, recombining them to generate different discrete conformations. This algorithm scales linearly rather than exponentially with the receptor's degrees of freedom. The method was first evaluated for its ability to identify known ligands of a hydrophobic cavity mutant of T4 lysozyme (L99A). Some 200000 molecules of the Available Chemical Directory (ACD) were docked against an ensemble of cavity conformations. Surprisingly, the enrichment of known ligands from among a much larger number of decoys in the ACD was worse than simply docking to the apo conformation alone. Large decoys, accommodated in the larger cavity conformations sampled in the ensemble, were ranked better than known small ligands. The calculation was redone with an energy correction term that considered the cost of forming the larger cavity conformations. Enrichment improved, as did the balance between high-ranking large and small ligands. In a second retrospective test, the ACD was docked against a conformational ensemble of thymidylate synthase. Compared to docking against individual enzyme conformations, the flexible receptor docking approach improved enrichment of known ligands. Including a receptor conformational energy weighting term improved enrichment further. To test the method prospectively, the ACD database was docked against another cavity mutant of lysozyme (L99A/M102Q). A total of 18 new compounds predicted to bind this polar cavity and to change its conformation were tested experimentally; 14 were found to bind. The bound structures for seven ligands were determined by X-ray crystallography. The predicted geometries of these ligands all corresponded to the observed geometries to within 0.7A RMSD or better. Significant conformational changes of the cavity were observed in all seven complexes. In five structures, part of the observed accommodations were correctly predicted; in two structures, the receptor conformational changes were unanticipated and thus never sampled. These results suggest that although sampling receptor flexibility can lead to novel ligands that would have been missed when docking a rigid structure, it is also important to consider receptor conformational energy.     

Accounting for global protein deformability during protein-protein and protein-ligand docking

Computational docking methods are valuable tools aimed to simplify the costly process of drug development and improvement. Most current approaches assume a rigid receptor structure to allow virtual screening of large numbers of possible ligands and putative binding sites on a receptor molecule. However, inclusion of receptor flexibility can be of critical importance since binding of a ligand can lead to changes in the receptor protein conformation that are sterically necessary to accommodate a ligand. Recent approaches to efficiently account for receptor flexibility during docking simulations are reviewed. In particular, accounting efficiently for global conformational changes of the protein backbone during docking is a still challenging unsolved problem. An approximate method has recently been suggested that is based on relaxing the receptor conformation during docking in pre-calculated soft collective degrees of freedom (M. Zacharias, Rapid protein-ligand docking using soft modes from molecular dynamics simulations to account for protein deformability: binding of FK506 to FKBP, Proteins: Struct., Funct., Genet. 54 (2004) 759-767). Test applications on protein-protein docking and on docking the inhibitor staurosporine to the apo-form of cAMP-dependent protein kinase A catalytic domain indicate significant improvement of docking results compared to rigid docking at a very modest computational demand. Accounting for receptor conformational changes in pre-calculated global degrees of freedom might offer a promising route to improve systematic docking screening simulations.     

Ligand-supported homology modeling of the human angiotensin II type 1 (AT(1)) receptor: insights into the molecular determinants of telmisartan binding

Angiotensin II type 1 (AT(1)) receptor belongs to the super-family of G-protein-coupled receptors, and antagonists of the AT(1) receptor are effectively used in the treatment of hypertension. To understand the molecular interactions of these antagonists, such as losartan and telmisartan, with the AT(1) receptor, a homology model of the human AT(1) (hAT(1)) receptor with all connecting loops was constructed from the 2.6 A resolution crystal structure (PDB i.d., 1L9H) of bovine rhodopsin. The initial model generated by MODELLER was subjected to a stepwise ligand-supported model refinement. This protocol involved initial docking of non-peptide AT(1) antagonists in the putative binding site, followed by several rounds of iterative energy minimizations and molecular dynamics simulations. The final model was validated based on its correlation with several structure-activity relationships and site-directed mutagenesis data. The final model was also found to be in agreement with a previously reported AT(1) antagonist pharmacophore model. Docking studies were performed for a series of non-peptide AT(1) receptor antagonists in the active site of the final hAT(1) receptor model. The docking was able to identify key molecular interactions for all the AT(1) antagonists studied. Reasonable correlation was observed between the interaction energy values and the corresponding binding affinities of these ligands, providing further validation for the model. In addition, an extensive unrestrained molecular dynamics simulation showed that the docking-derived bound pose of telmisartan is energetically stable. Knowledge gained from the present studies can be used in structure-based drug design for developing novel ligands for the AT(1) receptor.     

Insight into the mechanism of lipids binding and uptake by CD36 receptor

The membrane protein CD36 is a member of the class B scavenger receptor family. It plays a crucial role in some cardiovascular pathologies and metabolic diseases. Studying the mechanism of action of CD36 receptor is limited due to the absence of its tridimensional crystallized structure. The molecular docking method has allowed us to perform various simulation of the CD36 receptor interaction with their ligands involved in the development of some diseases. In this work, we predicted a tridimensional structure model of CD36 extracellular domain. In addition, we have achieved several tests of rigid and flexible docking by acting on residues proposed in previous experimental researches as essential in fixing of LFCAs. Furthermore, we have acted on regions that appear a key binding site of LFCAs. The physicoc hemical evaluation indicated the reliability of the proposed CD36 structure used for different molecular docking tests. Based on the docking outcome, we were able to propose the different steps of the mechanism allowing the interaction of fatty acids on CD36 receptor and their penetration into the cell cytoplasm. The obtained results and taking in consideration CD36 receptor as a therapeutic target will help us to suggest the mechanism by which an antagonist may inhibit this receptor by acting on its extracellular domain.     

Rescoring docking hit lists for model cavity sites: predictions and experimental testing

Molecular docking computationally screens thousands to millions of organic molecules against protein structures, looking for those with complementary fits. Many approximations are made, often resulting in low “hit rates.” A strategy to overcome these approximations is to rescore top-ranked docked molecules using a better but slower method. One such is afforded by molecular mechanics-generalized Born surface area (MM-GBSA) techniques. These more physically realistic methods have improved models for solvation and electrostatic interactions and conformational change compared to most docking programs. To investigate MM-GBSA rescoring, we re-ranked docking hit lists in three small buried sites: a hydrophobic cavity that binds apolar ligands, a slightly polar cavity that binds aryl and hydrogen-bonding ligands, and an anionic cavity that binds cationic ligands. These sites are simple; consequently, incorrect predictions can be attributed to particular errors in the method, and many likely ligands may actually be tested. In retrospective calculations, MM-GBSA techniques with binding-site minimization better distinguished the known ligands for each cavity from the known decoys compared to the docking calculation alone. This encouraged us to test rescoring prospectively on molecules that ranked poorly by docking but that ranked well when rescored by MM-GBSA. A total of 33 molecules highly ranked by MM-GBSA for the three cavities were tested experimentally. Of these, 23 were observed to bind—these are docking false negatives rescued by rescoring. The 10 remaining molecules are true negatives by docking and false positives by MM-GBSA. X-ray crystal structures were determined for 21 of these 23 molecules. In many cases, the geometry prediction by MM-GBSA improved the initial docking pose and more closely resembled the crystallographic result; yet in several cases, the rescored geometry failed to capture large conformational changes in the protein. Intriguingly, rescoring not only rescued docking false positives, but also introduced several new false positives into the top-ranking molecules. We consider the origins of the successes and failures in MM-GBSA rescoring in these model cavity sites and the prospects for rescoring in biologically relevant targets.     

应用死端排除法考虑分子对接中的侧链柔性

假设分子对接面的紧密堆积类似于蛋白质内部的紧密堆积,因此用于蛋白质内部的侧链构象预测方法,如死端排除法,可应用于分子对接面内的侧链构象预测。应用９个晶体结构对这一假设进行检验,结果表明假设基本正确。对２个蛋白酶与抑制剂的应用比较成功。９个配体中的７个有正确的均方根差的趋势。还发现受体结构的柔性较小,说明由于对接面的紧密堆积产生的侧链构象变化很小。根据这些结果,提出一个新的分子对接流程图,即在刚体对接后加入对接面中氨基酸残基的侧链构象预测。对一个蛋白酶与抑制剂的复合结构的应用表明对接中的正确解的信号与噪音比相对错误解增加了。     

应用死端排除法考虑分子对接中的侧链柔性

假设分子对接面的紧密堆积类似于蛋白质内部的紧密堆积,因此用于蛋白质内部的侧链构象预测方法,如死端排除法,可应用于分子对接面内的侧链构象预测。应用９个晶体结构对这一假设进行检验。结果表明假设基本正确。对２个蛋白酶和抑制剂的应用比较成功。９个配体中的７个有正确的均方根差的趋势。还发现受体结构的柔性较小,说明由于对接面的紧密堆积产生的侧链构象变化很小。根据这些结果,提出一个新的分子对接流程图,即在刚体对     

一种基于SVR的分辨近天然G蛋白耦联受体—配体构象的方法

蛋白质小分子对接的难点之一是从生成的大量候选结构中挑选出近天然构象。本文使用了一种基于SVR的方法来挑选RosettaLigand生成的GPCR—配体decoy构象中的近天然构象。首先,对已有数据训练得到一个SVR模型,预测decoy构象的LRMSD,然后依此挑选近天然构象。最终,比较了本文方法和RosettaLigand方法挑选出的近天然构象decoy的质量,结果优于RosettaLigand方法,结果表明了本文方法能够有效地挑选出近天然构象。     

An efficient computational method for calculating ligand binding affinities

Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, the docking scores are not sufficiently precise to represent the protein-ligand binding affinity. Here, we developed an efficient computational method for calculating protein-ligand binding affinity, which is based on molecular mechanics generalized Born/surface area (MM-GBSA) calculations and Jarzynski identity. Jarzynski identity is an exact relation between free energy differences and the work done through non-equilibrium process, and MM-GBSA is a semimacroscopic approach to calculate the potential energy. To calculate the work distribution when a ligand is pulled out of its binding site, multiple protein-ligand conformations are randomly generated as an alternative to performing an explicit single-molecule pulling simulation. We assessed the new method, multiple random conformation/MM-GBSA (MRC-MMGBSA), by evaluating ligand-binding affinities (scores) for four target proteins, and comparing these scores with experimental data. The calculated scores were qualitatively in good agreement with the experimental binding affinities, and the optimal docking structure could be determined by ranking the scores of the multiple docking poses obtained by the molecular docking process. Furthermore, the scores showed a strong linear response to experimental binding free energies, so that the free energy difference of the ligand binding (ΔΔG) could be calculated by linear scaling of the scores. The error of calculated ΔΔG was within ≈±1.5 kcal•mol⁻¹ of the experimental values. Particularly, in the case of flexible target proteins, the MRC-MMGBSA scores were more effective in ranking ligands than those generated by the MM-GBSA method using a single protein-ligand conformation. The results suggest that, owing to its lower computational costs and greater accuracy, the MRC-MMGBSA offers efficient means to rank the ligands, in the post-docking process, according to their binding affinities, and to compare these directly with the experimental values.     

Discrete molecular dynamics distinguishes nativelike binding poses from decoys in difficult targets

Virtual screening is one of the major tools used in computer-aided drug discovery. In structure-based virtual screening, the scoring function is critical to identifying the correct docking pose and accurately predicting the binding affinities of compounds. However, the performance of existing scoring functions has been shown to be uneven for different targets, and some important drug targets have proven especially challenging. In these targets, scoring functions cannot accurately identify the native or near-native binding pose of the ligand from among decoy poses, which affects both the accuracy of the binding affinity prediction and the ability of virtual screening to identify true binders in chemical libraries. Here, we present an approach to discriminating native poses from decoys in difficult targets for which several scoring functions failed to correctly identify the native pose. Our approach employs Discrete Molecular Dynamics simulations to incorporate protein-ligand dynamics and the entropic effects of binding. We analyze a collection of poses generated by docking and find that the residence time of the ligand in the native and nativelike binding poses is distinctly longer than that in decoy poses. This finding suggests that molecular simulations offer a unique approach to distinguishing the native (or nativelike) binding pose from decoy poses that cannot be distinguished using scoring functions that evaluate static structures. The success of our method emphasizes the importance of protein-ligand dynamics in the accurate determination of the binding pose, an aspect that is not addressed in typical docking and scoring protocols.     

Structural mechanism of S-adenosyl methionine binding to catechol O-methyltransferase

Methyltransferases possess a homologous domain that requires both a divalent metal cation and S-adenosyl-L-methionine (SAM) to catalyze its reactions. The kinetics of several methyltransferases has been well characterized; however, the details regarding their structural mechanisms have remained unclear to date. Using catechol O-methyltransferase (COMT) as a model, we perform discrete molecular dynamics and computational docking simulations to elucidate the initial stages of cofactor binding. We find that COMT binds SAM via an induced-fit mechanism, where SAM adopts a different docking pose in the absence of metal and substrate in comparison to the holoenzyme. Flexible modeling of the active site side-chains is essential for observing the lowest energy state in the apoenzyme; rigid docking tools are unable to recapitulate the pose unless the appropriate side-chain conformations are given a priori. From our docking results, we hypothesize that the metal reorients SAM in a conformation suitable for donating its methyl substituent to the recipient ligand. The proposed mechanism enables a general understanding of how divalent metal cations contribute to methyltransferase function.     

Three-dimensional models of alpha(2A)-adrenergic receptor complexes provide a structural explanation for ligand binding.

We have compared bacteriorhodopsin-based (alpha(2A)-AR(BR)) and rhodopsin-based (alpha(2A)-AR(R)) models of the human alpha(2A)-adrenengic receptor (alpha(2A)-AR) using both docking simulations and experimental receptor alkylation studies with chloroethylclonidine and 2-aminoethyl methanethiosulfonate hydrobromide. The results indicate that the alpha(2A)-AR(R) model provides a better explanation for ligand binding than does our alpha(2A)-AR(BR) model. Thus, we have made an extensive analysis of ligand binding to alpha(2A)-AR(R) and engineered mutant receptors using clonidine, para-aminoclonidine, oxymetazoline, 5-bromo-N-(4, 5-dihydro-1H-imidazol-2-yl)-6-quinoxalinamine (UK14,304), and norepinephrine as ligands. The representative docked ligand conformation was chosen using extensive docking simulations coupled with the identification of favorable interaction sites for chemical groups in the receptor. These ligand-protein complex studies provide a rational explanation at the atomic level for the experimentally observed binding affinities of each of these ligands to the alpha(2A)-adrenergic receptor.     

A flexible docking scheme to explore the binding selectivity of PDZ domains

Modeling of protein binding site flexibility in molecular docking is still a challenging problem due to the large conformational space that needs sampling. Here, we propose a flexible receptor docking scheme: A dihedral restrained replica exchange molecular dynamics (REMD), where we incorporate the normal modes obtained by the Elastic Network Model (ENM) as dihedral restraints to speed up the search towards correct binding site conformations. To our knowledge, this is the first approach that uses ENM modes to bias REMD simulations towards binding induced fluctuations in docking studies. In our docking scheme, we first obtain the deformed structures of the unbound protein as initial conformations by moving along the binding fluctuation mode, and perform REMD using the ENM modes as dihedral restraints. Then, we generate an ensemble of multiple receptor conformations (MRCs) by clustering the lowest replica trajectory. Using ROSETTA LIGAND , we dock ligands to the clustered conformations to predict the binding pose and affinity. We apply this method to postsynaptic density‐95/Dlg/ZO‐1 (PDZ) domains; whose dynamics govern their binding specificity. Our approach produces the lowest energy bound complexes with an average ligand root mean square deviation of 0.36 Å. We further test our method on (i) homologs and (ii) mutant structures of PDZ where mutations alter the binding selectivity. In both cases, our approach succeeds to predict the correct pose and the affinity of binding peptides. Overall, with this approach, we generate an ensemble of MRCs that leads to predict the binding poses and specificities of a protein complex accurately.     

Expanding GPCR homology model binding sites via a balloon potential: A molecular dynamics refinement approach

Homology modeling of G protein-coupled receptors is becoming a widely used tool in drug discovery. However, unrefined models built using the bovine rhodopsin crystal structure as the template, often have binding sites that are too small to accommodate known ligands. Here, we present a novel systematic method to refine model active sites based on a pressure-guided molecular dynamics simulation. A distinct advantage of this approach is the ability to introduce systematic perturbations in model backbone atoms in addition to side chain adjustments. The method is validated on two test cases: (1) docking of retinal into an MD-relaxed structure of opsin and (2) docking of known ligands into a homology model of the CCR2 receptor. In both cases, we show that the MD expansion algorithm makes it possible to dock the ligands in poses that agree with the crystal structure or mutagenesis data.     

Flexible relaxation of rigid-body docking solutions

Molecular Dynamics (MD) simulations have been performed on a set of rigid-body docking poses, carried out over 25 protein-protein complexes. The results show that fully flexible relaxation increases the fraction of native contacts (NC) by up to 70% for certain docking poses. The largest increase in the fraction of NC is observed for docking poses where anchor residues are able to sample their bound conformation. For each MD simulation, structural snap-shots were clustered and the centre of each cluster used as the MD-relaxed docking pose. A comparison between two energy-based scoring schemes, the first calculated for the MD-relaxed poses, the second for energy minimized poses, shows that the former are better in ranking complexes with large hydrophobic interfaces. Furthermore, complexes with large interfaces are generally ranked well, regardless of the type of relaxation method chosen, whereas complexes with small hydrophobic interfaces remain difficult to rank. In general, the results indicate that current force-fields are able to correctly describe direct intermolecular interactions between receptor and ligand molecules. However, these force-fields still fail in cases where protein-protein complexes are stabilized by subtle energy contributions.     

Deciphering the GPER/GPR30-agonist and antagonists interactions using molecular modeling studies,molecular dynamics,and docking simulations

The G-protein coupled estrogen receptor 1 GPER/GPR30 is a transmembrane seven-helix (7TM) receptor involved in the growth and proliferation of breast cancer. Due to the absence of a crystal structure of GPER/GPR30, in this work, molecular modeling studies have been carried out to build a three-dimensional structure, which was subsequently refined by molecular dynamics (MD) simulations (up to 120 ns). Furthermore, we explored GPER/GPR30’s molecular recognition properties by using reported agonist ligands (G1, estradiol (E2), tamoxifen, and fulvestrant) and the antagonist ligands (G15 and G36) in subsequent docking studies. Our results identified the E2 binding site on GPER/GPR30, as well as other receptor cavities for accepting large volume ligands, through GPER/GPR30 π–π, hydrophobic, and hydrogen bond interactions. Snapshots of the MD trajectory at 14 and 70 ns showed almost identical binding motifs for G1 and G15. It was also observed that C107 interacts with the acetyl oxygen of G1 (at 14 ns) and that at 70 ns the residue E275 interacts with the acetyl group and with the oxygen from the other agonist whereas the isopropyl group of G36 is oriented toward Met141, suggesting that both C107 and E275 could be involved in the protein activation. This contribution suggest that GPER1 has great structural changes which explain its great capacity to accept diverse ligands, and also, the same ligand could be recognized in different binding pose according to GPER structural conformations.